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1) Description of the model developed to estimate the ratio of ownerless dogs to owned
dogs in three zones in Iringa, Tanzania
In four wards of Iringa (Tanzania), all dogs that were accessible to vaccination either by central
point or by house-to-house vaccination, were marked with a collar. The four study wards were
divided into three zones, Gangilonga (zone 1), Kihesa (zone 2), and Makorongoni-Ilala combined
(zone 3). In four passages along the same transect lines per zone, data were collected on the
number of collared (vaccinated) and not-collared (unvaccinated) dogs encountered. From a
prior household census, the total number of owned dogs per ward was known. From an
additional survey on restriction practices and the durability of collars, also the probability of
restriction and of loss of collars was estimated. The Bayesian statistical model has been adapted
to the Iringa study site from an earlier study in N’Djaména (Chad) by Kayali et al. [35]
In the model, X1t(i)and X2t(i) are the number of owned dogs, collared and not-collared,
respectively, and Yt(i) is the number of ownerless (and not-collared) dogs recaptured in zone i
and on transect passage t. All collared dogs were owned since ownerless dogs were not brought
to the vaccination points. Not-collared dogs included both categories, owned dogs that had lost
the collar and also ownerless dogs as it was not possible to distinguish them. Therefore we
observed only the number of not-collared dogs Zt(i), instead of X2t(i) and Yt(i), where Zt(i)= X2t(i) +
Yt(i) and X2t(i) as well as Yt(i) are latent data. The total number of vaccinated (collared, owned)
dogs, Mv(i), in each zone i was known from the full household census conducted during the
study.
We assume that X1t(i), X2t(i) and Zt(i) follow binomial distributions with binomial recapture
probabilities, pt1(i), pt2(i) and pt3(i), respectively; that is,
X1t(i) ~Bn((1 – c1(i))*Mv(i), pt1 (i));
X2t(i) ~Bn((1 - c2(i))*Mu(i), pt2(i)) and
Zt(i) ~Bn((1 - c2(i))*Mu(i) + N(i), pt3(i)),
where c1(i) and c2(i) are confinement probabilities related to zone i for owned collared and
owned not-collared dogs, respectively; Mu(i) is the total number of unvaccinated owned dogs;
and N(i) is the total number of ownerless dogs in area i. To reduce the number of parameters of
the model, we assumed a common recapture probability, pt(i) for all dogs (collared owned, notcollared owned, and not-collared ownerless), that is, pt1(i)=pt2(i)= pt3(i) = pt(i).
We estimated the parameters of the model following Bayesian inference implemented by
Markov chain Monte Carlo simulation. Prior information about the model parameters was
obtained from the analysis of data collected during the household census and surveys on
restriction practices and durability of collars. Thus an initial estimate of the total owned dog
population M(i) = Mv(i) + Mu(i) in study zone i was taken applying the Petersen-Bailey formula for
direct sampling on captured (collared) –recaptured data observed during the household survey,
that is M(i) = Mv(i) *(ni + 1)/mi + 1 and standard error (M(i) ) = square root[Mv(i)*Mv(i)(ni + 1)*(ni –
mi)/(mi + 1)*(mi + 1)*(mi + 2)], where ni and mi are the numbers of recaptured dogs and
recaptured collared (vaccinated) dogs in the household survey in zone i, respectively. These
estimates specified the parameters of a normal prior distribution that was adopted for M(i). For
the final model data on M(i) and Mv(i) was collected from a full census during the household
visits (manuscript p. 6 line 30).
The parameter N(i) was expressed as a proportion aai of the total owned dogs, that is N(i) =
aai*M(i) where logit(aai)= a+ ei. A Uniform prior distribution was assumed for the overall
proportion a (on the logit scale), that is a~ U(0.0001, 0.1) and a Normal prior was assigned to ei,
ei ~N(0,) representing a random variation of the proportion in zone. We also assumed that
~Ga(0.1,0.1). The parameters of the above prior distributios were chosen by combining
the Petersen-Bailey estimate of the owned dogs with a rough estimate of the ownerless dog
population per zone obtained from the household questionnaire.
Uniform prior distributions were also adopted for the recapture probabilities pt(i). The
parameters of these distributions were chosen by assuming that recapture probabilities were
factored in three components: the area covered by the transect line (coverage), the probability
to encounter a specific dog provided the area is covered by the transect (encountering), and
the probability of the observer to actually record an encountered dog (recording). For each
component, uniform priors were adopted as explained below and shown in Table 3 of the main
document. The lower limit for the area coverage was calculated by dividing the area covered by
the transect (allowing 25 m along each side of the line to include a part of the road as well as
the yard of the compound next to the road) by the total area of the zone. The upper limit for
the area coverage was based on the assumption that more than 50% of the total area was
covered, as the transect passed every second parallel road, most compounds are along the
roads, and at intersections parallel streets could be seen. The limits of the uniform prior for the
encountering component are based on our observation that many dogs gather around their
compound and could therefore be seen. It is, however, a critical point in our assumption. We
concluded that recording was very high by comparing the counts of dogs recorded by the three
observers who drove together along each transect line.
Finally, beta distributions were adopted for the confinement probabilities c1(i) and c2(i),
c1(i)~Be(a1(i),b1(i)) and c2(i)~Be(a2(i),b2(i)). The proportion of dogs that, according to the household
survey, spend no time outside of the compound and were in compounds with secure fences
during the survey was taken as the mean of the beta distribution. The standard error of this
proportion was considered equal to the standard deviation of the prior. Table 1 shows the prior
distributions of confinement probabilities.
2) Model: Estimation of the proportion of feral dogs in a population
With Winbugs code
Model - Data from all zones pooled
model {
# X1t(i), X2t(i) and Zt(i) follow binomial distributions with binomial recapture probabilities, pt1(i),pt2(i) and pt3(i),
# respectively; that is,
for (i in 1:zone) {
for( t in 1 : T ) {
x1[i,t] ~ dbin(p[i,t],n1[i])
z[i,t] ~ dbin(p[i,t],n2[i])
x2[i,t] ~ dbin(p[i,t],n3[i])
#
p[i,t]~dunif(pmin[i],pmax[i])
p[i,t]~dbeta(arecap[i],brecap[i])
}
# lm probability of loosing the collar
n1[i]<-round((1-c1[i])*(1-lm)*Mv[i])
n2[i]<-round((1-c2[i])*(M[i]-Mv[i])+aa[i]*M[i])
n3[i]<-round((1-c2[i])*(M[i]-Mv[i]))
logit(aa[i])<-a+rand[i]
c1[i]~dbeta(a1[i],b1[i])
c2[i]~dbeta(a2[i],b2[i])
# Parameters of the Beta distribution assigned to the confinement probabilities of marked (c2) and
unmarked (c1) dogs. They expressed in terms of the mean and variances
a1[i]<-m1[i]*m1[i]*((1-m1[i])/(s1[i]*s1[i]))-m1[i]
b1[i]<-a1[i]*(1-m1[i])/m1[i]
a2[i]<-m2[i]*m2[i]*((1-m2[i])/(s2[i]*s2[i]))-m2[i]
b2[i]<-a2[i]*(1-m2[i])/m2[i]
arecap[i]<-mrecap[i]*mrecap[i]*((1-mrecap[i])/(srecap[i]*srecap[i]))-mrecap[i]
brecap[i]<-arecap[i]*(1-mrecap[i])/mrecap[i]
rand[i]~dnorm(0,tau)
}
tau~dgamma(0.1,0.1)
sigma<-1/tau
a~dunif(0.0001,0.1)
}
Data
T # no of transects per zone
x1: # no of captured marked owned
x2: # no of captured unmarked owned
z : # no of captured umnarked (owned + ownereless)
aa: proportion of stray to owned dogs
pmin/pmax : prior parameters of a Uniform distribution for the recapture probabilities
Mv : total no of vaccined(marked + owned) dogs
M : total no of owned dogs (vaccinated, marked+unvaccinated)
m1/s1: mean/sd of the prior distribution of the confinement probabililty
of marked (owned) dogs
m2/s2: mean/sd of the prior distribution of the confinement probabililty
of unmarked (owned) dogs
lm: probability of loosing the collar
list(T=4,x1=structure(.Data=c(14,17,2,3,24,18,12,11,12,10,3,3),.Dim=c(3,4))
,z=structure(.Data=c(16,14,5,2,6,3,3,1,13,18,1,4),.Dim=c(3,4)),zone=3,
mrecap=c(0.8,0.8,0.8),srecap=c(0.1,0.1,0.1),
m1=c(0.325,0.325,0.325),s2=c(0.395849,0.395849,0.395849),
m2=c(0.5180723,0.5180723,0.5180723),s1=c(0.3693691,0.3693691,0.3693691)
,Mv=c(719,826,402),M=c(1011,959,528), lm=0.14)
pmin=c(0.0504,0.0441,0.1134),pmax=c(0.54,0.5346,0.534
Inits
x2: # no of captured unmarked owned
aa: proportion of ownerless/owned
list(
arat=0.001,
c1 = c(
0.7048249938577898,0.5110506501002856,0.4185334068908934),
c2 = c(
0.7164662549432108,0.3176317120761007,0.2694882891509198),
p = structure(.Data = c(
0.2,0.2,0.2,0.2,
0.3415744049443895,0.3016802151411562,0.2868451663319444,0.2127375691434546,
0.2,0.2,0.2,0.2),
.Dim = c(3,4)),
rand = c(
-0.4156218641868892,-2.613612123466846,-1.029945024347904),
tau = 0.1534377222243299,
x2 = structure(.Data = c(
10.0,3.0,7.0,8.0,28.0,
31.0,28.0,22.0,20.0,31.0,
30.0,32.0),
.Dim = c(3,4)))
Results of the sensitivity analysis
Summary of the sensitivity analysis.
We varied the confinement and recapture probabilities from 0.2 to 0.4 and to 0.8 while keeping the other fixed.
Table 1: Relationship of median proportion of ownerless dogs in relation to confinement probability for all zones
Sensitivity
analyses
Parameter
Parameters of the prior distributions
Posterior median (and 95% BCI) for
the proportion (aa) of ownerless
dogs
GL
KH
MK/IL
GL
KH
MK/IL
1
Mean
0.2 (0.4)
(standard
deviation) of
confinement
probabilities
*
0.2 (0.4)
0.2 (0.4)
0.027
(0.0020.048)
0.012
(0.0030.023)
0.026
(0.00040.075)
2
Mean
0.4(0.4)
(standard
deviation) of
confinement
probabilities
0.4(0.4)
0.4(0.4)
0.001
(0.00.04)
0.002
(0.00.02)
0.003
(0.00.07)
3
Mean
0.8(0.8)
(standard
deviation) of
confinement
probabilities
0.8(0.8)
0.8(0.8)
0.028
0.011
(0.00005- (0.00010.048)
0.021)
a1[i]<-m1[i]*m1[i]*((1-m1[i])/(s1[i]*s1[i]))-m1[i]
b1[i]<-a1[i]*(1-m1[i])/m1[i]
0.037
(0.000020.0078)
Table 2 Relationship of the median proportion of ownerless dogs depending on the recapture probability.
Sensitivity Parameter
analyses
Parameters of the prior distributions
Posterior median (and 95% BCI) for the
proportion (aa) of ownerless dogs
GL
KH
MK/IL
GL
KH
MK/IL
1
Mean
0.2 (0.1)
(standard
deviation) of
recapture
probabilities
**
0.2 (0.1)
0.2 (0.1)
0.02 (0.00.072)
0.013
(0.00.036)
0.026
(0.00040.075)
2
Mean
0.4 (0.1)
(standard
deviation) of
recapture
probabilities
**
0.4 (0.1)
0.4 (0.1)
0.0 (0.00.029)
0.0 (0.00.012
0.0 (0.00.046)
3
Mean
0.8 (0.1)
(standard
deviation) of
recapture
probabilities
**
0.8 (0.1)
0.8 (0.1)
0.0 (0.00.0001)
0.0 (0.00.0 (0.00.000005) 0.001)
**arecap[i]<-mrecap[i]*mrecap[i]*((1-mrecap[i])/(srecap[i]*srecap[i]))-mrecap[i]
brecap[i]<-arecap[i]*(1-mrecap[i])/mrecap[i]
Interpretation of the sensitivity analysis.
The proportion of ownerless dogs is low in general (less than 2%). The proportion of ownerless dogs is sensitive to
the confinement probability with a minimum around 0.4 and higher proportions for confinement probabilities of 0.2
and 0.8. The proportion of ownerless dogs decreases with increased recapture probabilities. It is zero for recapture
probabilities higher than 0.4.
3) Alternative non-Bayesian approach to estimate vaccination coverage and the proportion of
unvaccinated dogs
An alternative algebraic approach to the Bayesian method use is provided to further clarify the
assessment of the average proportion of stray dogs and the vaccination coverage.
The following sub-populations are assessed during the household survey and the transect study.
Unvaccinated owned dogs U and stray dogs S cannot be distinguished in the street. Vaccinated dogs are
marked.
For a particular study zone:
U = total unvaccinated owned dogs
to be estimated from a Petersen-Bailey
sample
to be estimated from a Petersen-Bailey
sample
S = total stray dogs (considered all as unvaccinated)
unknown to be estimated from transect study
V = total vaccinated owned dogs
C = V /(V + U + S) = overall vaccination coverage
u = observed unvaccinated owned dogs
observed in house-to-house vaccination
campaign
observed in house-to-house vaccination
campaign
s = observed dogs (considered all as unvaccinated)
to be deducted from proportions of owned
dogs
x = confinement probability for vaccinated owned
dogs
Household survey (expressed as probability
distribution)
y = confinement probability for unvaccinated owned
dogs
Household survey (expressed as probability
distribution)
v = observed vaccinated owned dogs
Procedure:
The household survey is conducted until a sufficient large number of vaccinated dogs is counted to fulfill
the Petersen Bailey requirements. If 800 dogs are vaccinated (marked), we need to find 312 marked
dogs for a minimal vaccination coverage of 50% and a standard error of 5% (Table 3.1).
Table 3.1: Petersen Bailey recapture sample sizes
Population Initially
Vacc
Sample
marked
coverage
N
M
n
1600
1333
1143
1000
800
800
800
800
50
60
70
80
624
477
364
269
Marked
Sample
m
312
286
255
216
Estimated
Population
S. E. of
estimate
1607
1340
1149
1006
5% S. E.
80
67
57
50
10% S. E.
1600
1333
1143
1000
800
800
800
800
50
60
70
80
352
265
200
145
176
159
140
116
1611
1343
1153
1010
160
133
114
100
From recapture numbers we can derive the following statements:
c = v/o =v/(v+u) = observed vaccination coverage among the owned dogs (Household survey), or the
proportion of marked animals at household level. The value c stands also for the vaccination coverage at
household level
c=V/V+U
(1)
using this vaccination coverage and the known number of marked animals M we may estimate V and U
in the study zone on household level (provided the sampling is done in a random manner).
The transect study is done to estimate the proportion of stray dogs among the owned dogs. The transect
covers 20% of the vaccination zone. The observed dogs in the street can be categorized as follows:
p = 0.2 * x * V = number of observed vaccinated (marked) dogs seen in the street as a proportion of
all vaccinated dogs
(2)
q = 0.2 * (y * U) + 0.2 * S = the number of unmarked dogs seen in the street, composed of unvaccinated
owned dogs plus the stray dogs. These two categories cannot be distinguished.
An example of p and q from transects could look like this:
Zone
1
1
1
1
2
2
2
Transect
1
2
3
4
1
2
3
p
125
98
113
78
55
67
45
q
56
45
37
24
22
34
18
Ns = p + q = all observed dogs on the transect
Ns = 0.2 * ((x* V) + (y * U) + S))
U and S cannot be distinguished but U may be replaced by V from (1) and (2)
V= p/(0.2*x)
U = (V/c – V) = (p – cp)/(0.2*x*c)
Ns = p + (y * (p – cp)/(x*c) + 0.2*S
S = (Ns - p + (y * (p – cp)/(x*c))/ 0.2
S = q - (y * (p – cp)/(x*c))/ 0.2
C = V / V + U + S = overall vaccination coverage